An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems

Authors

  • Angela Noufaily The Open University
  • Doyo Enki The Open University
  • Paddy Farrington The Open University
  • Paul Garthwaite The Open University
  • Nick Andrews Health Protection Agency
  • Andre Charlett Health Protection Agency

DOI:

https://doi.org/10.5210/ojphi.v5i1.4497

Abstract

A large scale multiple statistical surveillance system for infectious disease outbreaks has been in operation in England and Wales for nearly two decades. This system uses a robust quasi-Poisson regression algorithm to identify aberrances in weekly counts of isolates reported to the Health Protection Agency. We review the performance of the system to reduce the number of false reports, while retaining good power to detect genuine outbreaks. Several improvements are suggested relating to the treatment of trends, seasonality, reweighting of baselines and error structure. The new system greatly reduces the numbers of alarms while maintaining good overall performance.

Author Biography

Angela Noufaily, The Open University

Angela Noufaily is a postdoc at The Open University in the United Kingdom. Her work involves outbreak detection in multiple surveillance systems.

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Published

2013-03-23

How to Cite

Noufaily, A., Enki, D., Farrington, P., Garthwaite, P., Andrews, N., & Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4497

Issue

Section

Poster Presentations